import pandas as pdimport re import numpy as npdf1 = pd.read_excel(r'D:\Ritesh_2022\Bhim\Feb\bhim_quad_data.xlsx')xxxxxxxxxxdf1xxxxxxxxxximport matplotlib.pyplot as pltfrom matplotlib.pyplot import figure%matplotlib inlinexxxxxxxxxxdf1xxxxxxxxxx# calculating and extraction data for NPS quadxxxxxxxxxxdf1['Imp in per'] =df1['Importance']*100xxxxxxxxxxdf1xxxxxxxxxxX2 =df1['NPS score']Y2 =df1['Imp in per']x_line =df1['NPS score'].mean()y_line =df1['Imp in per'].mean()xxxxxxxxxxdf1['Imp in per'] =df1['Importance']*100xxxxxxxxxxfrom matplotlib.pyplot import figurexxxxxxxxxxannotations=[i for i in df1['Categories']]xxxxxxxxxxnps =[i for i in df1['NPS score']]xxxxxxxxxxImportance = [i for i in df1['Importance']]xxxxxxxxxxImportance_in_percent = [i for i in df1['Imp in per']]xxxxxxxxxxannotations=[i for i in df1['Categories']]nps =[i for i in df1['NPS score']]Importance = [i for i in df1['Importance']]figure(figsize=(50, 60), dpi=80)plt.scatter(X2, Y2,c='magenta',s =500)plt.title('BHIM Quadrant',fontsize=25)plt.xlabel('NPS score',fontsize=25)plt.ylabel('Importnace',fontsize=25)plt.axhline(y = y_line, color = 'r', linestyle = '-')plt.axvline(x = x_line, color = 'r', linestyle = '-')plt.xticks([i for i in df1['NPS score']])plt.yticks([i for i in df1['Imp in per']])for i, label in enumerate(annotations): plt.annotate(label, (X2[i], Y2[i]),fontsize=25) # for i, label in enumerate(nps):# plt.annotate(label, (X2[i], Y2[i]),fontsize=15)# for i, label in enumerate(Importance):# plt.annotate(label, (X2[i], Y2[i]),fontsize=15)xxxxxxxxxxannotations=[i for i in df1['Categories']]nps =[i for i in df1['NPS score']]Importance = [i for i in df1['Importance']]figure(figsize=(30, 40), dpi=80)plt.scatter(X2, Y2,c='magenta',s =500)plt.title('BHIM Quadrant',fontsize=25)plt.xlabel('NPS score',fontsize=25)plt.ylabel('Importnace',fontsize=25)plt.axhline(y = y_line, color = 'r', linestyle = '-')plt.axvline(x = x_line, color = 'r', linestyle = '-')plt.xticks([i for i in df1['NPS score']])plt.yticks([i for i in df1['Imp in per']])for i, label in enumerate(annotations): plt.annotate(label, (X2[i], Y2[i]),fontsize=25) # for i, label in enumerate(nps):# plt.annotate(label, (X2[i], Y2[i]),fontsize=15)# for i, label in enumerate(Importance):# plt.annotate(label, (X2[i], Y2[i]),fontsize=15)xxxxxxxxxx# !pip install plotlyxxxxxxxxxximport plotly.express as pxxxxxxxxxxxdf2 =px.data.iris()xxxxxxxxxxdf2xxxxxxxxxxdf1xxxxxxxxxx# fig = px.scatter(data_frame=df1,x ='NPS score',y='Imp in per',color='Categories',size ='Importance')fig = px.scatter(data_frame=df1,x ='NPS score',y='Imp in per',text = 'NPS score',color='Categories')# fig.update_layout(shapes=[# fig.add_hline(y =x_line)fig.add_vline(x =x_line)fig.add_hline(y =y_line)# dict(# type= 'line',# yref= y_line,# xref= x_line# )# ])fig.show()xxxxxxxxxxfig = px.scatter(data_frame=df1,x ='NPS score',y='Imp in per',color='Categories',size ='Imp in per',text = 'NPS score')# fig = px.scatter(data_frame=df1,x ='NPS score',y='Imp in per',color='Categories')# fig.update_layout(shapes=[# fig.add_hline(y =x_line)fig.add_vline(x =x_line)fig.add_hline(y =y_line)# dict(# type= 'line',# yref= y_line,# xref= x_line# )# ])fig.show()xxxxxxxxxx